CN113078685B - Micro-grid dynamic partitioning method and system - Google Patents
Micro-grid dynamic partitioning method and system Download PDFInfo
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
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- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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Abstract
The invention discloses a micro-grid dynamic partitioning method which comprises the steps of collecting power grid topological data, and partitioning an initial range of a micro-grid based on a principle of power balance in the micro-grid and least load removal; according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets; and solving the microgrid dynamic optimization model to obtain an optimal microgrid range. A corresponding system is also disclosed. According to the method, the initial range of the microgrid is divided according to the topological data of the power grid, a dynamic optimization model of the microgrid is constructed by taking the minimum voltage deviation, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized based on the model.
Description
Technical Field
The invention relates to a micro-grid dynamic partitioning method and system, and belongs to the field of dispatching automation optimization dispatching.
Background
With the continuous development of new energy technologies, distributed power supplies such as photovoltaic power, wind power, energy storage and gas turbine are connected in large quantities, regional power grids are becoming a new generation of comprehensive energy internet with multi-source connection and source grid charge and storage coordinated operation, local regions already have microgrid operation characteristics, and can operate independently under the condition of losing system power supplies. At present, a regional power grid has proposed a main grid and microgrid integrated operation mode, which requires that the main grid and the microgrid can independently operate in normal and emergency states (such as a planned maintenance or fault mode), and challenges are provided for the regulation and control operation of the regional power grid, and dynamic division of the regional power grid microgrid needs to be realized.
Disclosure of Invention
The invention provides a micro-grid dynamic partitioning method and system, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a micro-grid dynamic partitioning method comprises the following steps,
acquiring power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
Collecting topological data of a power grid, dividing the initial range of the microgrid based on the principle of power balance and minimum load removal in the microgrid, and specifically comprising the following steps of,
collecting power grid topology data;
based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges and nodes corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of power sent by all distributed power supplies;
and obtaining the micro-grid initial range according to the edge set and the node set.
The micro-grid dynamic optimization model comprises the following steps of,
an objective function:
wherein F is an objective function; n is a radical of T Optimizing the number of time segments divided in the period; n is a radical of hydrogen p Is the total number of nodes, U, of the power grid i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of hydrogen line The number of times of adjustment for the tie line;
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical of hydrogen q The number of the index items is the running number of the microgrid; lambda q Membership function of the q-th operation index;
and power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s. the SOCmax 、S SOCmin Respectively an upper limit and a lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max The maximum charging power and the maximum discharging power of the stored energy are respectively; p BESSin (t) charging power for storing energy; p BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p line,max 、P line,min Are respectively P line (t) upper and lower limits; q line,max 、Q line,min Are respectively Q line (t) upper and lower limits; i is line,max 、I line,min Are respectively I line (t) upper and lower limits.
And solving the microgrid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal microgrid range.
A micro-grid dynamic partitioning system comprises a micro-grid,
an initial dividing module: acquiring power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
a model construction module: according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
an optimal acquisition module: and solving the microgrid dynamic optimization model to obtain an optimal microgrid range.
The initial partitioning module includes a first partition module for partitioning the image data,
the acquisition module is used for: collecting power grid topology data;
the first search module: based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topology data, storing all distributed power supply nodes into a node set, storing edges connected with all the distributed power supply nodes into an edge set, and calculating the sum of power sent by all the distributed power supplies;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges and bus nodes corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of power sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
The micro-grid dynamic optimization model constructed by the model construction module comprises the following steps,
an objective function:
wherein F is an objective function; n is a radical of hydrogen T Optimizing the number of time segments divided in the period; n is a radical of p Is the total number of grid nodes, U i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of line The number of times of adjustment for the tie line;
adjusting the states of the front switch and the rear switch for the connecting line respectively; n is a radical of q The number of the operating index items of the microgrid is set; lambda [ alpha ] q A membership function for the qth operation index;
and power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s. the SOCmax 、S SOCmin Respectively the upper limit and the lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max Respectively the maximum charging and discharging power of the stored energy; p BESSin (t) a charging power for storing energy; p is BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p line,max 、P line,min Are each P line (t) upper and lower limits; q line,max 、Q line,min Are respectively Q line (t) upper and lower limits; I.C. A line,max 、I line,min Are respectively I line (t) upper and lower limits.
And the optimal acquisition module is used for solving the dynamic optimization model of the microgrid by adopting a mixed integer linear programming method to acquire the optimal microgrid range.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a microgrid dynamic partitioning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a microgrid dynamic partitioning method.
The invention achieves the following beneficial effects: 1. according to the method, the initial range of the microgrid is divided according to the topological data of the power grid, a microgrid dynamic optimization model is constructed by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized on the basis of the model; 2. the microgrid dynamic optimization model aims at minimizing voltage deviation, minimizing switching operation times and optimizing operation index evaluation, improves the voltage quality of the microgrid, reduces switching abrasion, prolongs the sustainable operation time of the microgrid, improves the economy and improves the power supply reliability of the main network and the microgrid.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a dynamic partitioning method for a microgrid includes the following steps:
step 1, collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance and least load removal in the microgrid.
The specific process is as follows:
11) collecting power grid topology data, and setting the weight of the edge;
12) based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
13) based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
14) sequentially superposing load power according to the principle that the weight of the edge is from small to large, and respectively placing the edge and the node corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of the power sent by all distributed power supplies;
15) acquiring an initial range of the microgrid according to the edge set and the node set;
16) and checking the initial range of the microgrid, and checking whether the wireless circuit is overloaded and whether the bus voltage is out of limit.
And 2, comprehensively considering the safety constraint of the power grid and the resource adjustability constraint according to the initial range of the microgrid, and constructing a microgrid dynamic optimization model by taking the minimum voltage deviation, the minimum switching operation times and the optimal operation index evaluation as targets.
The micro-grid dynamic optimization model is as follows:
an objective function:
wherein F is an objective function; n is a radical of T To optimize the number of time segments divided within a cycle, one can consider the distribution for each time segmentThe output force, the energy storage output force and the load of the power supply are unchanged; n is a radical of hydrogen p Is the total number of grid nodes, U i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of line The number of times of adjustment for the tie line;
adjusting the states of the front switch and the rear switch for the connecting line respectively; n is a radical of q The number of the index items is the running number of the microgrid; lambda q Is a membership function of the q-th operation index with a membership of [0,1 ]]The larger the membership degree is, the better the index is, the better the microgrid operation performance is, and the longer the microgrid sustainable operation time is.
Constraint conditions are as follows:
and (3) power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s SOCmax 、S SOCmin Respectively the upper limit and the lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max The maximum charging power and the maximum discharging power of the stored energy are respectively related to the total capacity of the stored energy; p is BESSin (t) charging power for storing energy; p BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p is line,max 、P line,min Are respectively P line (t) upper and lower limits; q line,max 、Q line,min Are each Q line (t) upper and lower limits; i is line,max 、I line,min Are respectively I line (t) upper and lower limits.
And 3, solving a micro-grid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal micro-grid range.
A nonlinear term exists in an objective function of the microgrid dynamic optimization model, and a binary variable alpha with the value of 0 or 1 is introduced i Remove t period | U i Absolute value sign of 1 |:
|U i -1|＝(2α i -1)(U i -1)
wherein alpha is i To indicate U i Binary variable of-1 sign, when U i -1 is not negative, then α i Is 1, otherwise, when U i 1 is negative, then α i Is 0.
The above relationship can be expressed linearly as:
in which continuous variables are used
Replace alpha i (U i -1) when α is i When the value is 0, the number of the first electrode is,
is 0 and has U i -1 is negative when α i When the number of the carbon atoms is 1,
and U is i -1 is not negative, M is constantly greater than U i -a constant of 1.
The second part of the objective function carries out linearization conversion similarly:
the mixed integer linear programming method ensures the global optimality of the resulting solution.
According to the method, the initial range of the microgrid is divided according to topological data of the power grid, a dynamic optimization model of the microgrid is constructed by taking the minimum voltage deviation, the minimum switching operation times and the optimal operation index evaluation as targets, and the dynamic division of the microgrid is effectively realized on the basis of the model; meanwhile, the microgrid dynamic optimization model aims at minimizing voltage deviation, minimizing switching operation times and optimizing operation index evaluation, improves the voltage quality of the microgrid, reduces switching abrasion, prolongs the sustainable operation time of the microgrid, improves the economy and improves the power supply reliability of the main network and the microgrid.
A micro-grid dynamic partitioning system comprises a micro-grid,
an initial dividing module: and collecting power grid topological data, and dividing the initial range of the microgrid based on the principle of power balance in the microgrid and least load removal.
The initial division module comprises a first division module and a second division module,
the acquisition module is used for: collecting power grid topology data;
the first search module: based on the power grid topology data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight value connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topology data, storing all distributed power supply nodes into a node set, storing edges connected with all the distributed power supply nodes into an edge set, and calculating the sum of power sent by all the distributed power supplies;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges corresponding to loads and bus nodes into an edge set and a node set until the superposed load power is not less than the sum of powers sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
A model construction module: and constructing a microgrid dynamic optimization model according to the initial range of the microgrid by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets.
The micro-grid dynamic optimization model constructed by the model construction module comprises the following steps,
an objective function:
wherein F is an objective function; n is a radical of T Optimizing the number of time segments divided in the period; n is a radical of hydrogen p Is the total number of nodes, U, of the power grid i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of line The number of times of adjustment for the tie line;
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical of q The number of the operating index items of the microgrid is set; lambda q A membership function for the qth operation index;
and power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s SOCmax 、S SOCmin Respectively the upper limit and the lower limit of the energy storage charge state;
energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max Respectively the maximum charging and discharging power of the stored energy; p BESSin (t) a charging power for storing energy; p is BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p line,max 、P line,min Are respectively P line (t) upper and lower limits; q line,max 、Q line,min Are each Q line (t) upper and lower limits; I.C. A line,max 、I line,min Are respectively I line (t) toAnd a lower limit.
And the optimal acquisition module is used for solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to acquire an optimal micro-grid range.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a microgrid dynamic partitioning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a microgrid dynamic partitioning method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention as filed.
Claims (8)
1. A micro-grid dynamic partitioning method is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal; according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
the micro-grid dynamic optimization model comprises the following steps of,
an objective function:
wherein F is an objective function; n is a radical of T Optimizing the number of time segments divided in the period; n is a radical of p Is the total number of nodes, U, of the power grid i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of line The number of times of adjustment for the tie line;
adjusting the states of the front and rear switches for the tie lines respectively; n is a radical of hydrogen q The number of the operating index items of the microgrid is set; λ q is a membership function of the qth operation index;
and power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s SOCmax 、S SOCmin Respectively the upper limit and the lower limit of the energy storage charge state; energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max The maximum charging power and the maximum discharging power of the stored energy are respectively; PB (PB) ESSin (t) charging power for storing energy; p is BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p line,max 、P line,min Are respectively P line (t) upper and lower limits;Q line,max 、Q line,min are each Q line (t) upper and lower limits; i is line,max 、Il ine,min Are respectively I line (t) upper and lower limits;
and solving the dynamic optimization model of the microgrid to obtain the optimal microgrid range.
2. The dynamic microgrid partitioning method according to claim 1, characterized in that: collecting power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance and minimum load removal in the microgrid, wherein the specific process is to collect the power grid topological data;
based on the power grid topology data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight value connected with each bus node into an edge set;
based on the power grid topology data, storing all distributed power supply nodes into a node set, storing edges connected with all the distributed power supply nodes into an edge set, and calculating the sum of power sent by all the distributed power supplies;
sequentially superposing load power according to the principle that the weight of the edge is from small to large, and respectively placing the edge and the node corresponding to the load into an edge set and a node set until the superposed load power is not less than the sum of the power sent by all distributed power supplies;
and obtaining the micro-grid initial range according to the edge set and the node set.
3. The dynamic partitioning method for the microgrid according to claim 1, characterized in that: and solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to obtain an optimal micro-grid range.
4. A micro-grid dynamic partitioning system is characterized in that: comprises the steps of (a) preparing a substrate,
an initial dividing module: acquiring power grid topological data, and dividing an initial range of the microgrid based on a principle of power balance in the microgrid and least load removal;
a model construction module: according to the initial range of the microgrid, constructing a microgrid dynamic optimization model by taking the minimum voltage offset, the minimum switching operation times and the optimal operation index evaluation as targets;
the micro-grid dynamic optimization model constructed by the model construction module comprises the following steps of,
an objective function:
wherein F is an objective function; n is a radical of T Optimizing the number of time segments divided in the period; n is a radical of p Is the total number of nodes, U, of the power grid i (t) is the ith grid node voltage; CB is the total number of switches; n is a radical of hydrogen line The number of times of adjustment for the tie line;
adjusting the states of the front switch and the rear switch for the connecting line respectively; n is a radical of q The number of the operating index items of the microgrid is set; λ q is a membership function of the qth operation index;
and power balance constraint:
∑P TRANS (t)+∑P GEN (t)-∑P LOAD (t)＝0
wherein, P TRANS (t)、P GEN (t)、P LOAD (t) power transmitted between the main network and the microgrid, power of all distributed power supplies in the microgrid and load power in the microgrid are respectively;
energy storage state of charge constraint:
S SOCmin ≤S SOC (t)≤S SOCmax
wherein S is SOC (t) is the energy storage state of charge; s. the SOCmax 、S SOCmin Respectively an upper limit and a lower limit of the energy storage charge state; energy storage charge and discharge power constraint:
P BESSin (t)≤P BESSin,max
P BESSout (t)≤P BESSout,max
wherein, P BESSin,max 、P BESSout,max The maximum charging power and the maximum discharging power of the stored energy are respectively; PB (PB) ESSin (t) charging power for energy storage；P BESSout (t) is the discharge power of the stored energy;
and (3) line margin constraint:
P line,min ≤P line (t)≤P line,max
Q line,min ≤Q line (t)≤Q line,max
I line,min ≤I line (t)≤I line,max
wherein, P line (t)、Q line (t)、I line (t) active, reactive and current on the line respectively; p line,max 、P line,min Are each P line (t) upper and lower limits; q line,max 、Q line,min Are respectively Q line (t) upper and lower limits; i is line,max 、Il ine,min Are respectively I line (t) upper and lower limits;
an optimal acquisition module: and solving the dynamic optimization model of the microgrid to obtain the optimal microgrid range.
5. The dynamic microgrid partitioning system of claim 4, wherein: the initial division module comprises an acquisition module: collecting power grid topology data;
the first search module: based on the power grid topological data, storing all bus nodes into a node set, and storing the edge with the minimum setting weight connected with each bus node into an edge set;
a power sum calculation module: based on the power grid topological data, storing all distributed power source nodes into a node set, storing edges connected with all the distributed power source nodes into an edge set, and calculating the sum of power sent by all the distributed power sources;
a second search module: sequentially superposing load power according to the principle that the edge weight is from small to large, and respectively placing edges corresponding to loads and bus nodes into an edge set and a node set until the superposed load power is not less than the sum of powers sent by all distributed power supplies;
a result module: and obtaining the micro-grid initial range according to the edge set and the node set.
6. The dynamic microgrid partitioning system of claim 4, wherein: and the optimal acquisition module is used for solving the micro-grid dynamic optimization model by adopting a mixed integer linear programming method to acquire an optimal micro-grid range.
7. A computer readable storage medium storing one or more programs, wherein: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-3.
8. A computing device, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-3.
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